Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis
Year: 2018
Type: article
Abstract: M. tuberculosis grows slowly and is challenging to work with experimentally compared with many other bacteria. Although microtitre plates have the potential to enable high-throughput phenotypic testing of M. tuberculosis, they can be difficult to read and interpret. Here we present a software package, the Automated Mycobacterial Growth Detection Algorithm (AMyGDA), that measures how much M. tuberculosis is growing in each well of a 96-well microtitre plate. The plate used here has serial dilutions of 14 anti-tuberculosis drugs, thereby permitting the MICs to be elucidated. The three participating laboratories each inoculated 38 96-well plates with 15 known M. tuberculosis strains (including the standard H37Rv reference strain) and, after 2 weeks' incubation, measured the MICs for all 14 drugs on each plate and took a photograph. By analysing the images, we demonstrate that AMyGDA is reproducible, and that the MICs measured are comparable to those measured by a laboratory scientist. The AMyGDA software will be used by the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) to measure the drug susceptibility profile of a large number (>30000) of samples of M. tuberculosis from patients over the next few years.
Source: Microbiology
DOI: 10.1099/mic.0.000733
Authors: Philip W. Fowler, Ana Luíza Gibertoni Cruz, Sarah J. Hoosdally, Lisa Jarrett, Emanuele Borroni, Matteo Chiacchiaretta, Priti Rathod, Sarah Lehmann, Nikolay Molodtsov, Timothy M. Walker, Esther Robinson, Harald Hoffmann, Timothy E. A. Peto, Daniela Maria Cirillo, Grace E. Smith, Derrick W. Crook
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1628705071, NIHR Senior Investigator Award, University of Oxford - to improve control of tuberculosis, and facilitate the WHO’s initiative to eliminate tuberculosis by 2050, by better, faster and more targeted trea..., NIHR Senior Investigator Award, Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC), G6868616705, G7196713885, Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC)
OpenAlex ID: W2896589331